Quantum Dot Energized Heterogeneous Multi-Sensor with Edge Fulgurated Decision Accomplisher

ABSTRACT

Aspects described herein relate to a centralized computing system that interacts with a plurality of data centers, each having an edge server. Each edge server obtains sensor information from a plurality of sensors and processes the sensor information to detect an imminent shutdown and sends emergency data to a centralized processing entity when detected. In order to make a decision, the edge server processes the sensor data based on dynamic sensor thresholds and dynamic prioritizer data by syncing with the centralized computing system. Because of the short time duration to report emergency data before an imminent complete shutdown, an edge server may utilize a quantum data pipeline and quantum data storage as a key medium for all data transfer in a normal condition and at the time of emergency for internally transporting processed sensor data and providing the emergency data to the centralized processing entity.

TECHNICAL FIELD

One or more aspects of the disclosure generally relate to computingdevices, computing systems, and computer software. In particular, one ormore aspects of the disclosure generally relate to computing devices,computing systems, and computer software that may be used to monitor andprocess sensor information from one or data centers.

BACKGROUND

Natural calamities or man-made events that affect data centers arecrucial to the functioning of a business. However, emergency dataindicative of the reasons for the malfunctioning of a data center may belost or not even transmitted to a centralized computing center (often inthe cloud) before a complete shut-down. Hence, a fallback strategy maybe difficult to achieve in such a situation.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosure. The summary is not anextensive overview of the disclosure. It is neither intended to identifykey or critical elements of the disclosure nor to delineate the scope ofthe disclosure. The following summary merely presents some concepts ofthe disclosure in a simplified form as a prelude to the descriptionbelow.

Aspects described herein may relate to a centralized computing systemthat interacts with a plurality of data centers, each having an edgeserver. Each edge server obtains sensor information from a plurality ofsensors and processes the sensor information to detect an imminentshutdown and sends emergency data to a centralized processing entitywhen detected.

With an aspect of the embodiments, an edge server of a data centerprocesses the sensor data based on dynamic sensor thresholds and dynamicprioritizer data by syncing with the centralized computing system.

With an aspect of the embodiments, an edge server may utilize a quantumdata pipeline and quantum data storage as a key medium for all datatransfer in a normal condition and at a time of emergency for internallytransporting processed sensor data and providing the emergency data tothe centralized processing entity.

With another aspect of the embodiments, an edge server of a data centercomprises a sensor storage configured to receive sensor data from aplurality of sensors, to store the sensor data, and to send the sensordata to a data filter, a local prioritizer configured to validatewhether the sensor data is above or below a threshold value and togenerate a validation signal about the validation, a local collaboratorengine, a data storage, and a data pipeline. The data filter isconfigured to obtain the sensor data from the sensor storage, to filterthe sensor data based on dynamic sensor threshold data, and to providethe filtered sensor data to the local collaborator engine. The localizedcollaborator engine is configured to prioritize collected sensor dataand to present the prioritized data to a local fuzzy probabilisticcontroller logic (FPCL). The local FPCL is configured to obtain thecollected sensor data from the localized collaborator engine and toprocess the collected sensor data using fuzzy logic on the collectedsensor data to obtain localized decision output data. The data storageis configured to store the localized decision output data, where thedata pipeline connects an output of the local FPCL and the data storageand is configured to transport the localized decision output data fromthe local FPCL to the data storage.

With another aspect of the embodiments, a data pipeline comprises aquantum data pipeline and a data storage comprises a quantum datastorage.

With another aspect of the embodiments, an edge server of a data centercomprises a data combiner configured to combine the localized decisionoutput data with processed sensor data in which the combined data istransported over the data pipeline to the data storage.

With another aspect of the embodiments, a quantum dot panel is embeddedwithin at least one sensor (some or all) of the plurality of sensors ofa data center so that the at least one sensor is self-powered.

With another aspect of the embodiments, a centralized computing systemmay be implemented completely or partially with computing cloudservices.

With another aspect of the embodiments, a local prioritizer of an edgeserver is configured to synchronize synchronization data with a centralprioritizer of a centralized computing system.

With another aspect of the embodiments, a local prioritizer of an edgeserver receives a dynamic sensor threshold value for the sensor datafrom a centralized computing system. The local prioritizer adjusts thereceived dynamic sensor threshold based on a localized edge sensorthreshold value.

With another aspect of the embodiments, a local fuzzy probabilisticcontroller logic (FPCL) is configured to hierarchically combine thecollected sensor data from random sets, where the sensor data may bepartitioned into heterogeneous sensor data and homogeneous sensor data.

With another aspect of the embodiments, a gateway is configured tomonitor data traffic from a plurality of sensors of a data center and todetect whether an exception occurs. A monitoring and control engine(MCE) is configured to generate a signal to one of the plurality ofsensors associated with the exception.

With another aspect of the embodiments, a centralized computing systemis connected to a plurality of edge servers of a data center. Thecentralized computing system comprises a central prioritizer configuredto synchronize synchronization data with a localized prioritizer of eachedge server, a central collaborator engine configured to collect edgedata from the plurality of edge servers and to prioritize the collectededge data, and a global fuzzy probabilistic controller logic (FPCL)configured to obtain prioritized collected edge data from the centralcollaborator engine and to apply fuzzy logic on the prioritizedcollected edge data to obtain global decision output data.

With another aspect of the embodiments, a central collaborator engine ofa centralized computing system consumes edge data from the plurality ofedge servers and provides the dynamic sensor threshold data and dynamicprioritization data from the edge data.

With another aspect of the embodiments, a central collaborator engineutilizes a self-mutating algorithm based calculator to determine thedynamic sensor threshold data and dynamic prioritization data.

With another aspect of the embodiments, a global FPCL of a centralizedcomputing system is configured to hierarchically combine the prioritizedcollected edge data from a central collaborator engine.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIG. 1 illustrates a plurality of data centers to provide edge data to acentralized computing system and a reporting/resolution layer inaccordance with one or more example embodiments.

FIG. 2 illustrates a data center in accordance with one or more exampleembodiments.

FIG. 3 illustrates an edge server of a data center as shown in FIG. 2according to one or more illustrative embodiments.

FIG. 4 illustrates a centralized computing system as shown in FIG. 1 inaccordance with one or more example embodiments.

FIG. 5 illustrates an exemplary Fuzzy Probabilistic Controller Logic(FPCL) as shown in FIG. 3 according to one or more illustrativeembodiments.

FIG. 6 illustrates processing of sensor data at an edge server accordingto one or more illustrative embodiments.

FIG. 7 illustrates processing of edge data from one or more edge serversby a centralized computing system according to one or more illustrativeembodiments.

FIG. 8 illustrates a reporting/resolution layer as shown in FIG. 1according to one or more illustrative embodiments.

FIG. 9 illustrates a computing circuit that may be incorporated intoedge servers, centralized computing device, or reporting/resolutionlayer as shown in FIG. 1 in accordance with one or more exampleembodiments.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which the claimed subject matter may be practiced. It isto be understood that other embodiments may be utilized, and thatstructural and functional modifications may be made, without departingfrom the scope of the present claimed subject matter.

A data center may experience an emergency situation (for example,electrical power outage or fire accident). In such a situation,emergency data is last minute data recorded from various sensors.Furthermore, the order of occurrence of sensor data may be important inunderstanding the root cause of issue. For example, a fire caused by afuel/diesel generator may be identified by the ordering: power outage,usage of generator, and fire alarm. If such information is lost during apower outage or fire accident, analyzing the root cause may be difficultto construct.

With some embodiments, emergency data may be transferred through aquantum dot pipeline that provides fast transmission rate at low voltagelevels. Such an approach is often adequate to sufficiently operate inthe environment during an emergency situation.

Embodiments are directed to a centralized computing system thatinteracts with a plurality of data centers, each having an edge server.Each edge server obtains sensor information from a plurality of sensorsand processes the sensor information to detect an imminent shutdown andsends emergency data to a centralized processing entity when detected.In order to make a decision that an emergency is imminent, the edgeserver processes the sensor data based on dynamic sensor thresholds anddynamic prioritizer data by syncing with the centralized computingsystem.

Because of the short time duration to report emergency data before animminent complete shutdown, an edge server may utilize a quantum datapipeline and quantum data storage as a key medium for all data transferin a normal condition, and including at the time of emergency conditionas well, for internally transporting processed sensor data and providingthe emergency data to the centralized processing entity.

FIG. 1 illustrates a plurality of data centers 102-103 that provideemergency data to centralized computing system 101 (which may beimplemented in a cloud) and reporting/resolution layer 108 (which may beimplemented with a computing device) when a shutdown is expected inaccordance with one or more example embodiments.

As shown in FIG. 1 , data center 102 comprises edge server 104 andsensors 106.

When data center 102 anticipates a shutdown (for example, a loss ofelectrical power) based on the sensor data provided by sensors 106, edgeserver 104 transmits emergency data 155 to reporting/resolution layer108 and/or centralized computing system 101.

Data centers 102 and 103 provide edge data (for example, processedsensor data and emergency data as will be discussed) via paths 151 and152, respectively, to centralized computing system 101 so thatcentralized computing system 101 can determine centralized data for datacenters 102 and 103.

Centralized data (for example, dynamic prioritization data and dynamicsensor thresholds as will be discussed) for edge servers 104 and 105 andcentralized computing system 101 are synchronized via sync paths 153 and154. The values of sensor threshold and prioritization data fromcentralized computing system 101 may be calculated and then transmittedto and stored in edge server 104 as a dynamic value to arrive atenhanced functioning for a high alert mechanism needed for criticalfunctions. For example, the sensor threshold data from different edgeservers may be processed by centralized computing system 101 (forexample in a “cloud” using computing cloud services) and may be added toa localized threshold value of edge server 104.

Centralized computing system 101 processes edge data from edge servers102 and 103 and provides centralized data 157 to reporting/resolutionlayer 108 about the status of data centers 102 and 103. Additionally,data centers 102 and 103 may provide emergency data 155 and 156,respectively) when data centers 102 or 103 encounters an outagesituation (for example, electrical power or fire). With someembodiments, reporting/resolution layer 108 may be implemented on thesame platform as centralized computing system 101 or on a separatecomputing device.

FIG. 2 illustrates data center 102, which comprises sensors 106 a-106 n,IoT gateway 201, and edge server 104.

Sensors 106 a-106 n comprises security sensors, fire sensors, carbonemission sensors, temperature sensors, light sensors, smoke sensors,weather forecast information, and other types of sensors that areneeded. As will be discussed, sensor data from homogeneous andheterogeneous sensors may be combined via randomized hierarchicalcombining to obtain a decision output about an emergency situation.Also, some of sensors 106 a-106 n may measure the same physicalcharacteristic (for example, fire or temperature) but may be distributedthroughout different regions of data center 102.

Homogeneous sensors refer to hierarchal combining of the same type ofsensors, for example one temperature sensor versus another temperaturesensor at different locations. Heterogeneous sensors refer to hierarchalcombining of different types of sensors, for example, fire versus smokesensors.

With some embodiments, at least one of sensors 106 a-106 n may beelectrically self-powered by one or more quantum dot panels (quantum dotsolar cells) embedded within the sensor. Moreover, the one or morequantum dot panels may convert heat produced inside data center 102 toelectrical power for the at least one of sensors 106 a-106 n. The aboveapproach may provide additional robustness during an emergency situationwhen external electrical power is diminished or lost.

Sensor data is obtained via sensor bus 251 and conveyed through IOTGateway 201 where data traffic cop (DTC) 202 monitors the traffic forthe sensor data. Exceptions arising due to data transmission is handledthrough a monitoring and control engine (MCE) 203. Information about theexceptions may then be transmitted to reporting and resolution layer108. Otherwise, sensor data 252 is sent to edge server 104 forprocessing as will be discussed.

When MCE 203 encounters exception 253 with one of sensors 106 a-106 n,MCE 203 may generate a signal to the corresponding sensor in orderaddress the exemption. For example, when the corresponding sensor is nottransmitting sensor data, MCE 203 may ping the sensor to activate it orrestart the corresponding sensor via an actuator (for example, to starta cooling fan or valve).

FIG. 3 illustrates edge server 104 of data center 102 as shown in FIGS.1 and 2 according to one or more illustrative embodiments.

All of sensor data 252 is stored in data storage 301 (for example adatabase) at edge server 104. The stored sensor data 351 a is thenfiltered by data filter 302 according to dynamic sensor threshold dataand prioritization data from local prioritizer 307. Also, stored sensordata 352 b is sent to local prioritizer 307 for validation. For example,local prioritizer 307 may verify whether the sensor data is above orbelow a threshold value depending on the sensor type. A sensor typicallyhas a corresponding threshold value that may be determined by edgeserver 104 based on synchronization data from centralized computingsystem 101 and adjusted by edge server 104 based on characteristics ofdata center 101. Different sensors typically have different thresholdvalues, where some are constant across geographical location (forexample, carbon emission) while some vary based on the geographicallocation (for example, weather) and local policies.

Local prioritizer 307 also provides prioritization data 354 tocollaborator engine 306 based on synchronization data received fromcentralized computing system 101. Prioritization data 354 gauges theimportance of different sensor data with respect to an outage situation.For example, centralized computing system 101 may determineprioritization data 354 based on sensor data obtained previous andduring an emergency situation from edge servers 104 and 105.

Local collaborator engine 306 may decide to send emergency data when ashutdown of data center 102 is expected. This decision may be based onhigh priority alerts such as a fire alarm. It then sends the emergencydata to the cloud (for example, central computing system 101).

With some embodiments, local prioritizer 307 provides the priority logic(including a dynamic value with priority components added and rankedthrough the cloud). For example, the logic may prioritize the sensors inan order of importance.

Local prioritizer 307 may support priority logic, in which a dynamicvalue for prioritization data 354 is determined by priority componentsbeing added and ranked through the cloud (for through centralizedcomputing system 101).

Local prioritizer 307 and global prioritizer 401 may use emergencyinformation, company policy, industry standards, and/or regulations. Forexample, some regions within a country may be prone to more earthquakes,where an earthquake occurs in Location A (corresponding to a highseismic zone). This information may be utilized by local prioritizer307. This information may then be passed to global prioritizer 401 to beincluded as lessons learnt and used for a decision making processthrough global FPCL 403.

Filtered data 353 is then sent to local collaborator engine 306 in orderto prioritize filtered data 353 based on the prioritization data 354.Prioritized sensor data 355 is then sent to local fuzzy probabilisticcontroller logic (FPCL) 303.

Local collaborator engine 306 may prioritize filtered data 353 based onpolices and standards.

Local FPCL 303 processes prioritized sensor data 355 by hierarchicallycombining it and applying fuzzy logic, where prioritized sensor data 355is based on sensor data from sensors 106 a-106 n as shown in FIG. 1 . Aswill be discussed, sensor data from both homogeneous and heterogeneousare paired and fuzzy logic is applied to each pair in order to obtainthe next layer of the hierarchical combining. These FPCL operations arerepeated until localized decision output 356 is obtained.

Although local FPCL 303 may be presented with near real time sensordata, FPCL 303 may be, alternatively or in conjunction with, presentedwith historical sensor data and/or emergency data. For example,near-real time temperature sensor data may be paired with correspondingsensor data from the previous hour or day.

With some embodiments, local FPCL 303 may further randomize thehierarchical combining. For example, local FPCL 303 may select randomsensor sets from both homogeneous and heterogeneous types of prioritizedsensor data 355.

Local FPCL 303 may hierarchically use real time dynamic sensor thresholddata, dynamic prioritizer data, and history data to arrive at a rightdecision as reflected in localized decision output 356.

FIG. 5 shows an example of hierarchical combining by local FPCL 303 aswell as global FPCL 403 as will be discussed at FIG. 4 . Sensor datafrom carbon emission and fire sensors (corresponding to heterogeneoussensors) are combined while sensor data from temperature sensors(corresponding to homogeneous sensors) are combined according to fuzzylogic to obtain an output (“no issues”, “LOW”, “MEDIUM”, or “HIGH”). Adigital representation of the FPCL output is stored in quantum datastorage 304 via quantum data pipeline 303. For example, binaryrepresentations ‘00’, ‘01’, ‘10’, and ‘11’ may correspond to fuzzy logicvalues “no issues”, “LOW”, “MEDIUM”, and “HIGH”, respectively. However,as will be appreciated by one of ordinary skill in the art, embodimentsmay use additional binary bits to represent additional fuzzy logicvalues.

The resulting output from local FPCL 303 and global FPCL 403 may be usedon a local or global basis, respectively, to initiate an appropriateaction. For example, when the output of local FPCL 303 is “High,” edgeserver 104 may switch on fans if a heat sensor is above a thresholdlimit or may generate an alert if a carbon emission sensor is high.

With some embodiments, local FPCL 303 uses hierarchically real timedynamic sensor threshold data, dynamic prioritizer data, and historydata to arrive at a decision. Processing by edge server 104 by localFPCL 303 may be used to process high critical alerts in premises(localized) enabling a quick edge decision.

The output of local FPCL 303 (along with possibly other edge data suchas processed sensor data (for example, from local collaborator engine306 and/or local prioritizer 307 although not explicitly shown in FIG. 3) is then directed through quantum data pipeline 303 and then stored inquantum data storage 304. Quantum data storage 304 may require only avery low voltage to operate properly and consumes less electrical powerthan typical storage devices, thus providing critical data up until thelast moment of the shutdown emergency and enabling enhanced decisionmaking through reporting and resolution layer 108.

Quantum data pipeline 304 may comprise a quantum wire that may transmitdata at a speed of 100 GB/second. Quantum storage 305 may comprise aquantum dot storage having a density of 1 TB/cm², which is approximately20 times larger than typical magnetic storage.

Quantum data pipeline 304 and quantum storage 305 may utilize quantumdots (QDs) that are synthetic nano-scale crystals that transportelectrons. They are typically zero dimensional crystallinesemiconducting nanoparticles with diameters less than 10 nm and may befabricated as a metalloid crystalline core.

Storage and data transmission using quantum dots enables seamlesstransmission of data even in the case of low voltage of operation. Thisapproach may also prevent loss of data when there is power shutdown thatis crucial for decision making. High speed quantum dot communicationthrough quantum data pipeline 304 provides real time data transfer at ahigh speed.

Quantum data pipeline 304 and quantum data storage 305 enables that theFPCL output (decision) be transmitted and then saved to the “cloud” (forexample, centralized computing system 101 and/or reporting/resolutionlayer 108), thus circumventing crucial data loss during contingency asit operates even at a low voltage.

While FIG. 3 explicitly shows only the transport of the FPCL output(localized decision output 356), from quantum data pipeline 304 toquantum data storage 305, quantum data pipeline 304 and quantum datastorage 305 may provide a medium for all data transfer (corresponding toedge data 151) in a normal condition and including, at the time ofemergency as well, for internally transporting processed sensor data andproviding the emergency data to a centralized entity such as centralizedprocessing system 101. For example, transported data may includeprocessed sensor data and emergency data from edge server 104 as well aslocalized decision output 356.

While embodiments may utilize quantum data pipeline 304 and quantum datastorage 305, some embodiments may utilize other technologies for datatransport and data storage. For example, while current optical fiber maytransmit at less than 10 GB of data per second, embodiments may utilizetechnologies (such as quantum dot storage and quantum wire) that supporta recording density of 1 TB/cm² and a data transmission rate of 100GB/second.

FIG. 4 illustrates centralized computing system 101 (which may besupported in a “cloud”) as shown in FIG. 1 in accordance with one ormore example embodiments. For example, centralized computing system 101may be completely or partially implemented using computing cloudresources/services.

Central collaborator engine 402 obtains edge data 151 and 152 (forexample, processed sensor data and emergency data) from edge servers 104and 105, respectively.

Edge data from edge servers 104 and 105 across multiple locations arecollated at central collaborator engine 402, resulting in centralizeddecision making.

Centralized collaboration engine 402 consumes data from all edge servers(for example edge data 151 and 152 from edge servers 104 and 105,respectively) and provides critical threshold and prioritization data.Outputs from various edge servers across multiple locations are collatedby collaborator engine 402 and in turn leading to a centralized decisionmaking.

Central prioritizer 401 may obtain calculated prioritization data andsensor threshold data from the central collaborator engine 402 tosynchronize with edge servers 104 and 105.

The dynamic sensor threshold and prioritization are calculated bycentralized computing system 101 and then transmitted and stored at edgeserver 104 as a dynamic value to arrive at enhanced functioning of thehigh alert mechanism needed for critical functions.

Different types of sensors typically have different threshold values.For example, one type has a threshold value that is constant acrossgeographical location, for example carbon emission. A second type has athreshold value that typically changes based on the geographicallocation such as weather and local policies. With the first type,centralized computing system 101 can modify the threshold values andupdate them by syncing with edge servers 104 and 105, respectively, viasync paths 153 and 154, respectively. With the second type, centralizedcomputing system 101 (for example, at central collaborator engine 402)obtains edge data 151 and 152 from the edge servers and utilizes thehistorical data and analyzes preventive measures obtained fromhistorical logs to determine the dynamic threshold values. With someembodiments, edge data 151 and 152 may be processed by artificialintelligence techniques such as machine learning providing a roboticdecision maker or threshold setter.

With some embodiments, centralized computing system 101 may support asensor value threshold and prioritization calculator in order determinedynamic value of sensor threshold/prioritization data that is derivedbased on learnings/emergency situations from other locations.Centralized computing system 101 may also support a threshold andprioritization self-mutating algorithm based the calculator.

Global FPCL 403 obtains collected edge data 451 from centralcollaborator engine 402 and processes the collected edge 451 similar tolocal FPCL 303 as shown in FIG. 3 . For example, global FPCL mayhierarchically combine collected edge data 451 and may further randomizethe combining the prioritized collected edge data.

While the functioning of global FPCL 403 and local FPCL 303 is similar,the scope is different. Local FPCL 303 typically uses data from thelocal data center 102 while global FPCL 403 uses data from across thelocations and not specific to one data center.

Global decision output 453 (the output of global FPCL 403) may then beprovided to reporting/resolution layer 108.

Using the historical data in global FPCL 403 and analyzing thepreventive measures taken from a historical log can assist indetermining sensor thresholds at the cloud. A portion of theinterpretation may need human intervention but the robotic decisionmaker may be employed to do the same. Moreover, additional informationsuch as processed data and decision making information from centralprioritizer 401 and central collaborator engine 402 may also be providedto reporting/resolution 108 as shown in FIGS. 1 and 8 .

FIG. 6 illustrates processing of sensor data at an edge server such asedge server 104 as shown in FIG. 3 .

Sensor database 601 (for example, corresponding to sensor storage 301 asshown in FIG. 3 ) stores sensor data from sensors 106 a-106 n forsubsequent processing by local prioritizer 603 and local collaboratorengine 605.

At block 602, local prioritizer 603 validates whether sensor data isindicative of an emergency (for example, a fire alarm indicative of afire at data center 102). If so, emergency data is sent to local FPCL606, where emergency data 652 may be included in the determination ofthe decision output and/or included in the data transported over thequantum data pipeline to quantum storage quantum dots storage 608 viadata combiner 607.

In addition, local prioritizer 603 determines validates sensor data withrespect to the dynamic sensor threshold (either above or below athreshold depending on the type of sensor). If so, the sensor data ispassed to local collaborator engine 605; otherwise, the sensor data isignored.

Sensor data to local collaborator engine 605 may be further partitionedinto regions (for example, regions A, B, and C) so that localcollaborator engine 605 appropriately prioritizes sensor data acrossdifferent locations within a data center. For example, data center 102may have a plurality of temperature sensors. Local collaborator engine605 may prioritize a subset of the plurality of temperature sensors toensure that temperature measurements are represented over all of thedesired locations of the data center.

Local collaborator engine 605 presents prioritized collected edge dataand applies fuzzy logic on the prioritized collected edge data in arandomized hierarchal manner to obtain a global decision output data.Data combiner 607 may then combine the global decision output data withprocessed sensor data and/or emergency data, so that the combined datacan be stored in quantum dots storage 608, which can be provided tocentral computing system 101 as edge data.

As previously discussed, local prioritizer 603 communicates with centralcomputing system 101 via path 654 in order to synchronize the dynamicprioritization data and the dynamic sensor threshold data.

FIG. 7 illustrates processing of edge data 751 from one or more edgeservers by centralized computing system 101 as shown in FIG. 4 .

Central collaborator engine 701 collects edge data 751 from theplurality of edge servers and prioritizes the collected edge data toform prioritized collected edge data 752. Also, central collaboratorengine may consume edge data 751 and determine the dynamic sensorthreshold data and dynamic prioritization data from the edge data.Central collaborator engine 701 may utilize a self-mutating algorithmbased calculator to determine the dynamic sensor threshold data anddynamic prioritization data.

Central collaborator engine 701 presents the prioritized collected edgedata 752 to global FPCL 702, which then hierarchically combinesprioritized collected edge data 752. As previously discussed, globalFPCL 702 obtains global decision output 753 and presents it to datacombiner 703 so that it may be combined with processed edge data.Central computing system 101 may present global decision output 753and/or processed edge data to reporting/resolution layer 108.

Central collaborator engine 701 may uniformly distribute prioritizedcollected edge data 752 over the plurality of edge servers in order toavoid biasing the data with respect to any particular edge server.Consequently, the resulting processed edge data will be betterrepresentative of the plurality of data centers. For example, a balancedrepresentation over all of the edge servers helps to ensure that globaldecision output 753 generated by global FPCL 702 is not biased by anyone particular data center.

Report generator 704 may then generate a report that is indicative ofthe data centers based on the combined data. Also, information from thereport may be presented to report analyzer 705. Report analyzer 705 maythen provide modifications of the synchronization data 754 to centralprioritizer 706 based on analyzing the report information.

As previously discussed, central prioritizer 706 synchronizesynchronization data (for example, dynamic sensor threshold data anddynamic prioritization data) with a localized prioritizer of each edgeserver over path 654.

With some embodiments, report analyzer 705 may apply machine learning todetermine the modifications to the synchronization data.

FIG. 8 illustrates reporting/resolution layer 108 as shown in FIG. 1according to one or more illustrative embodiments. Reporting/resolutionlayer 108 may be implemented by a computing device as a part of acomputing system, or partially by cloud computing services.

Reporting/resolution layer 108 typically supports a plurality offunctions. For example, reporting/resolution layer 108 may supportinventory management 801 a, infra health check-up 801 b, alerts 802,actuators 803, an emission map 804, a dashboard 805, andco-location/fire department notification 806.

Reporting/resolution layer 108 may monitor and present data indashboards and reports. It may also alert a co-location data center totake over, or may alert the fire department, or alert a respectivetechnicians.

Reporting/resolution layer 108 may also remotely trigger one or moreactuators associated with one or more sensors 106 a-106 n at data center102 as shown in FIG. 2 . For example, reporting/resolution layer 108 mayinitiate one or more cooling fans associated with one of the sensors fora temperature rise.

FIG. 9 illustrates a computing circuit that may be incorporated intoedge servers 102 and 103, centralized computing system 101, orreporting/resolution layer 108 as shown in FIG. 1 in accordance with oneor more example embodiments.

Referring to FIG. 1 , centralized computing system 101, edge servers 102and 103, or reporting/reporting layer 108 may comprise processing device901, memory device 904, input interface 902, and output interface 903.Processing device 901 may execute computer-readable instructions storedat memory device 304 in order to execute processes to process the sensordata. Processing device 901 may receive sensor data from sensors 601a-601 n and process the sensor data. Processed sensor data,synchronization data, and/or emergency data may be exposed via outputinterface 903.

Various aspects described herein may be embodied as a method, anapparatus, or as computer-executable instructions stored on one or morenon-transitory and/or tangible computer-readable media. Accordingly,those aspects may take the form of an entirely hardware embodiment, anentirely software embodiment (which may or may not include firmware)stored on one or more non-transitory and/or tangible computer-readablemedia, or an embodiment combining software and hardware aspects. Anyand/or all of the method steps described herein may be embodied incomputer-executable instructions stored on a computer-readable medium,such as a non-transitory and/or tangible computer readable medium and/ora computer readable storage medium. Additionally or alternatively, anyand/or all of the method steps described herein may be embodied incomputer-readable instructions stored in the memory and/or othernon-transitory and/or tangible storage medium of an apparatus thatincludes one or more processors, such that the apparatus is caused toperform such method steps when the one or more processors execute thecomputer-readable instructions. In addition, various signalsrepresenting data or events as described herein may be transferredbetween a source and a destination in the form of light and/orelectromagnetic waves traveling through signal-conducting media such asmetal wires, optical fibers, and/or wireless transmission media (forexample, air and/or space).

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one of ordinary skill in the art willappreciate that the steps illustrated in the illustrative figures may beperformed in other than the recited order, and that one or more stepsillustrated may be optional in accordance with aspects of thedisclosure.

What is claimed is:
 1. A centralized computing system interacting with aplurality of edge servers of associated data centers, the centralizedcomputing system comprising: a central prioritizer configured tosynchronize synchronization data with a localized prioritizer of eachedge server of the plurality of edge servers; a central collaboratorengine configured to collect edge data from the plurality of edgeservers and to prioritize the collected edge data; and a global fuzzyprobabilistic controller logic (FPCL) configured obtain the prioritizedcollected edge data from the central collaborator engine and to applyfuzzy logic on the prioritized collected edge data to obtain a globaldecision output data.
 2. The centralized computing system of claim 1,wherein the synchronization data comprises dynamic prioritization dataand dynamic sensor threshold data.
 3. The centralized computing systemof claim 2, wherein the central collaborator engine consumes the edgedata from the plurality of edge servers and provides the dynamic sensorthreshold data and the dynamic prioritization data from the edge data.4. The centralized computing system of claim 3, wherein the centralcollaborator engine utilizes a self-mutating algorithm based calculatorto determine the dynamic sensor threshold data and the dynamicprioritization data.
 5. The centralized computing system of claim 1,wherein the global FPCL is configured to hierarchically combine theprioritized collected edge data to obtain the global decision outputdata.
 6. The centralized computing system of claim 5, wherein the globalFPCL is configured to randomize the hierarchical combining.
 7. Thecentralized computing system of claim 5, wherein the prioritizedcollected edge data is partitioned into heterogeneous sensor data andhomogeneous sensor data.
 8. The centralized computing system of claim 1,wherein the central collaborator engine is configured to uniformlydistribute the prioritized collected edge data over the plurality ofedge servers.
 9. The centralized computing system of claim 1, furthercomprising a data combiner configured to combine the global decisionoutput data and processed edge data to obtain combined data.
 10. Thecentralized computing system of claim 9, further comprising: a reportgenerator configured to obtain the combined data and to generate areporting output indicative of a status of the associated data centers.11. The centralized computing system of claim 10, further comprising: areport analyzer configured to obtain the reporting output and provideany modifications of the synchronization data to the centralprioritizer.
 12. The centralized computing system of claim 11, whereinthe report analyzer applies machine learning to the reporting output toobtain the modifications to the synchronization data and to present themodifications to the central prioritizer.
 13. The centralized computingsystem of claim 1, wherein the centralized computing system presents theglobal decision output data to a reporting/resolution layer.
 14. Amethod for a centralized computing system processing edge data from aplurality of edge servers associated with a plurality of data centers,the method comprising: synchronizing synchronization data with a localprioritizer of each edge server of the plurality of edge servers;collecting edge data from the plurality of edge servers; prioritizingthe collected edge data in accordance with the synchronization data toobtain prioritized collected edge data; and applying hierarchicalrandomized fuzzy logic to the prioritized collected edge data to obtainglobal decision output data.
 15. The method of claim 14, furthercomprising: consuming the edge data from the plurality of edge serversand obtaining the synchronization data, wherein the synchronization datacomprises dynamic sensor threshold data and dynamic prioritization datafrom the edge data.
 16. The method of claim 15, further comprising:utilizing a self-mutating algorithm based calculator to determine thedynamic sensor threshold data and the dynamic prioritization data. 17.The method of claim 14, further comprising: partitioning the prioritizedcollected edge data into heterogeneous sensor data and homogeneoussensor data; and applying the hierarchal randomized fuzzy logic to thepartitioned prioritized collected edge data.
 18. The method of claim 14,further comprising: obtaining combined data from the global decisionoutput data and processed edge data; and generating modifications to thesynchronization data from the combined data.
 19. One or morenon-transitory computer-readable media storing instructions that, whenexecuted by a centralized computing system comprising at least oneprocessor, and memory, cause the centralized computing system to:synchronize synchronization data with a local prioritizer of each edgeserver of a plurality of edge servers; collect edge data from theplurality of edge servers; prioritize the collected edge data inaccordance with the synchronization data to obtain prioritized collectededge data; and apply hierarchical randomized fuzzy logic to theprioritized collected edge data to obtain global decision output data.20. The one or more non-transitory computer-readable media of claim 19storing instructions that, when executed by the centralized computingsystem cause the centralized computing system to: obtain combined datafrom the global decision output data and processed edge data; andgenerate modifications to the synchronization data from the combineddata.